An Introduction To Statistical Learning

An Introduction to Statistical Learning provides an accessible overview of the champaign of statistical learning, an essential toolset for making sense of the vast together with complex data sets that take emerged in fields ranging from biology to finance to marketing to astrophysics inward the by 20 years. This book presents approximately of the virtually important modeling together with prediction techniques, along amongst relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, back up vector machines, clustering, deep learning, survival analysis, multiple testing, together with more. Color graphics as well as real-earth examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques past practitioners inward scientific discipline, manufacture, in addition to other fields, each chapter contains a tutorial on implementing the analyses and methods presented inward R, an extremely popular open up root statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani too Friedman, second edition 2009), a pop reference book for statistics and auto learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a degree accessible to a much broader audience. This volume is targeted at statisticians too not-statisticians alike who wish to use cut-edge statistical learning techniques to analyze their data. The text assumes solely a previous class in linear regression together with no noesis of matrix algebra.

This Second Edition features new chapters on deep learning, survival analysis, too multiple testing, as well equally expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, in addition to matrix completion. R code has been updated throughout to ensure compatibility.